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Machine Intelligence

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SS 2017 - SoSe 2021

English

Machine Intelligence

12

Obermayer, Klaus

Benotet

Mündliche Prüfung

English

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351300 FG Neuronale Informationsverarbeitung

Keine Angabe

Kontakt


MAR 5-6

Groiß, Camilla

sekr@ni.tu-berlin.de

https://www.bccn-berlin.de/courses-and-modules.html

Lernergebnisse

In this module, participants will gain knowledge about: - basic concepts, their theoretical foundation and the most common algorithms used in machine learning and artificial intelligence - strengths and limitations of the different paradigms They will be enabled to: - apply methods and algorithms to real world problems - be aware of performance criteria - critically evaluate results obtained with those methods - modify algorithms to new tasks at hand - develop new algorithms according to the paradigms presented in this course.

Lehrinhalte

Part 1: Artificial neural networks. Connectionist neurons, the multilayer perceptron, radial basis function networks, learning by empirical risk minimization, gradient-based optimization, overfitting and underfitting, regularization techniques, deep networks, applications to classification and regression problems. Part 2: Learning theory and support vector machines. Elements of statistical learning theory, learning by structural risk minimization, the C Support Vector Machine, kernels and non-linear decision boundaries, SMO optimization, the P-SVM. Part 3: Probabilistic methods. Reasoning under uncertainty and Bayesian inference; graphical models, graphs vs. distributions, and belief propagation; generative models; Bayesian inference and neural networks; non-parametric density estimation; parametric density estimation and maximum likelihood methods. Part 4: Reinforcement learning (MDP, value iteration, policy iteration, Q-learning). Part 5: Projections methods. Principal Component Analysis and Kernel-PCA; independent component analysis and blind source separation techniques (Infomax, Fast-ICA, ESD). Part 6: Stochastic optimization. Simulated annealing, mean-field techniques. Part 7: Clustering and embedding. K-means clustering, pairwise clustering methods, self-organizing maps for central and pairwise data.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Machine Intelligence IVL0434 L 866WiSeKeine Angabe2
Machine Intelligence IIVL0434 L 867SoSeKeine Angabe2
Machine Intelligence IUE0434 L 866WiSeKeine Angabe2
Machine Intelligence IIUE0434 L 867SoSeKeine Angabe2
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Machine Intelligence I (VL):

AufwandbeschreibungMultiplikatorStundenGesamt
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.02.0h30.0h
60.0h(~2 LP)

Machine Intelligence II (VL):

AufwandbeschreibungMultiplikatorStundenGesamt
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.02.0h30.0h
60.0h(~2 LP)

Machine Intelligence I (UE):

AufwandbeschreibungMultiplikatorStundenGesamt
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.06.0h90.0h
120.0h(~4 LP)

Machine Intelligence II (UE):

AufwandbeschreibungMultiplikatorStundenGesamt
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.06.0h90.0h
120.0h(~4 LP)
Der Aufwand des Moduls summiert sich zu 360.0 Stunden. Damit umfasst das Modul 12 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

The lecture part consists of teaching in front of the class. Participants are expected to rehearse topics after class, using their class notes as well as recommended book chapters, in preparation for the exercises and tutorials. Homework assignments are given on a regular basis, and must be usually solved within one or two weeks. These assignments cover analytical & mathematical exercises as well as numerical simulations & programming exercises. Working in small groups of two to three students is encouraged. Homework assignments and their solutions are discussed during the tutorial. In addition, selected topics presented during the lecture are rehearsed by the tutor as needed. The first tutorials cover a brief mathematics primer, and recommendations are provided for students for the module “individual studies”, if deficits in their mathematical knowledge become obvious.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Wünschenswerte Voraussetzungen für die Teilnahme zu den Lehrveranstaltungen: Mathematical knowledge: Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Basic programming skills. Good command of the English language.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Voraussetzung
Leistungsnachweis » [NI] Machine Intelligence II - Hausaufgabe «
Leistungsnachweis »[NI] Machine Intelligence I - Hausaufgabe«

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Oral exam

Sprache(n)

English

Dauer/Umfang

30 Min.

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
2 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Winter- und Sommersemester.

Maximale teilnehmende Personen

Dieses Modul ist nicht auf eine Anzahl Studierender begrenzt.

Anmeldeformalitäten

Enrollment to the module is handled in the first class of each module component (cf. 3). Students must be present in person. The module components Machine Intelligence I (lecture with exercises) and Machine Intelligence II (lecture with exercises) can be taken in any order, i.e. students may also start the module in the summer term. To be allowed to do the oral exam, students must achieve (seperately) at least 60% of the points awarded for homework in each of the two lectures. Students of the Master program in Computational Neuroscience have to register for the final oral exam at least three working days prior to the examination date. Registration has to be done with the examination office (Prüfungsamt) of TU Berlin. For students from other programs, other regulations may apply. Please consult the examination regulations (Prüfungsordnung) of your program. sekr@ni.tu-berlin.de

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  verfügbar

 

Literatur

Empfohlene Literatur
01. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, 2006. (recommended)
02. Duda, Hart, Stock, Pattern Classification, Wiley, 2000. (recommended)
03. Haykin, Neural Networks, Prentice Hall, 1998. (recommended)
04. Kohonen, Self-Organizing Maps, Springer-Verlag, 1997. (recommended)
05. Schölkopf, Smola, Learning with Kernels, MIT Press, 2002. (recommended)
06. Russel, Norvig, Artificial Intelligence, Prentice Hall, 2003, Chapters 13 and 14. (recommended)
07. Cichocki, Amari, Adaptive Blind Signal and Image Processing, Wiley, 2002. (additional)
08. Cowell, Dawid, Lauritzen, Spiegelhalter, Probabilistic Networks and Expert Systems, Springer Verlag, 1999. (additional)
09. Hyvärinen, Karhunen, Oja, Independent Component Analysis, Wiley, 2001. (additional)
10. Jordan (Editor), Learning in Graphical Models, MIT Press, 1999. (additional)
11. Kay, Fundamentals of Statistical Signal Processing - Vol. I: Estimation Theory, Prentice Hall, 1993. (additional)
12. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996. (additional)
13. Vapnik, Statistical Learning Theory, Wiley, 1998. (additional)
One or two specific book chapters are assigned / recomended to every topic of the lecture. This list of recommendations is explained during the first class of every module component and is available via TU Berlin’s ISIS platform

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Dieses Modul findet in keinem Studiengang Verwendung.

Sonstiges

Das Modul ist exclusiv Studenten des Studiengangs „Computational Neuroscience (MSc) vorbehalten. Studenten anderer Studiengänge sollten statt dessen die beiden Module „Machine Intelligence 1“ und „Machine Intelligence 2“ belegen. The modul is restricted to students of „computational neuroscience (Msc)“. All other students should instead register for the courses „machine intelligence 1“ and „machine intelligence 2“.